Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Generating and matching hashes of multimedia content

a multimedia content and hashing technology, applied in the field of multimedia content generation and matching hashes, can solve the problems of prohibitive search strategy and exhaustive search, and achieve the effect of overcoming the complexity of np-complete search and robust hashing

Active Publication Date: 2008-10-23
GRACENOTE
View PDF67 Cites 73 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

[0007]It is a general object of the invention to provide a robust hashing technology. More particularly, it is a first object of the invention to provide a method and arrangement for extracting a limited number of hashing bits from multimedia content. The hashing bits are robust, but not in a sense that the probability of bit errors is zero. It is known that non-exact pattern matching (i.e. searching for the most similar hash value in the database) is NP-complete. In layman's terms, this means that the best search strategy is an exhaustive search, which is prohibitive in many applications dealing with large databases. Therefore, a second object of the invention is to provide a method and arrangement that overcomes this NP-complete search complexity.

Problems solved by technology

In layman's terms, this means that the best search strategy is an exhaustive search, which is prohibitive in many applications dealing with large databases.

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Generating and matching hashes of multimedia content
  • Generating and matching hashes of multimedia content
  • Generating and matching hashes of multimedia content

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0021]Before describing a preferred embodiment, a general description of considerations underlying this invention will be elucidated.

[0022]Two signals (audio, video, image) can differ quite drastically (e.g. by compression) in a signal theoretical sense, whereas they are perceptually indistinguishable. Ideally, a hash function mimics the behavior of the human auditory system (HAS) or human visual system (HVS), i.e. it produces the same hash signal for content that is considered the same by the HAS / HVS. However, many kinds of processing (compression, noise addition, echo addition, D / A and A / D conversion, equalization etc.) can be applied to the signal and there is no algorithm that is able to mimic the HAS / HVS perfectly. A complicating factor is that even the HAS / HVS varies from person to person as well as in time, and even the notion of one single HAS / HVS is untenable. Also, the classical definition of a hash does not take time into account: a robust hash should not only be able to ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

Hashes are short summaries or signatures of data files which can be used to identify the file. Hashing multimedia content (audio, video, images) is difficult because the hash of original content and processed (e.g. compressed) content may differ significantly. The disclosed method generates robust hashes for multimedia content, for example, audio clips. The audio clip is divided (12) into successive (preferably overlapping) frames. For each frame, the frequency spectrum is divided (15) into bands. A robust property of each band (e.g. energy) is computed (16) and represented (17) by a respective hash bit. An audio clip is thus represented by a concatenation of binary hash words, one for each frame. To identify a possibly compressed audio signal, a block of hash words derived therefrom is matched by a computer (20) with a large database (21). Such matching strategies are also disclosed. In an advantageous embodiment, the extraction process also provides information (19) as to which of the hash bits are the least reliable. Flipping these bits considerably improves the speed and performance of the matching process.

Description

CLAIM OF PRIORITY[0001]This application is Continuation of U.S. application Ser. No. 10 / 073,772 filed Feb. 11, 2002, which claims the priority benefit of EP Application No. 01202720.7 filed on Jul. 17, 2001, which in turn claims the priority benefit of EP Application No. 01200505.4 filed on Feb. 12, 2001, all of which are incorporated herein by reference.FIELD OF THE INVENTION[0002]The invention relates to a method and arrangement for generating a hash signal identifying an information signal. The invention also relates to a method and arrangement for matching such a hash signal with hash signals stored in a database.BACKGROUND OF THE INVENTION[0003]Hash functions are generally known in the field of cryptography, where they are used, inter alia, to identify large amounts of data. For instance, in order to verify correct reception of a large file, it suffices to send the hash value (also referred to as signature) of that file. If the returned hash value matches the hash value of the ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(United States)
IPC IPC(8): H04L9/00H04L9/06G06F17/30G06K9/00G09C1/00G10H1/00H03M7/30H04H1/00H04H60/31H04H60/37H04H60/56
CPCG06F17/30787G06T2201/0065G06K9/00744G06T1/0028G06T1/005G06T2201/0051G06T2201/0061G10H1/0058G10H2210/295G10H2240/061H03M7/30H04H60/56H04H2201/90H04N19/467G06F17/30802H04H60/37G06F16/7834G06F16/683G06F16/785G06V20/46G06F18/00
Inventor HAITSMA, JAAP ANDREMARIA KALKER, ANTONIUS ANDRIANUS CORNELISJOZEF BAGGEN, CONSTANT PAUL MARIEOOSTVEEN, JOB CORNELIS
Owner GRACENOTE
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products